From Business Regulation and Economic Performance, Ch. 5, edited by N. Loayza and L. Servén, The World Bank, Washington D.C.

INFORMALITY IN LATIN AMERICA AND THE CARIBBEAN*

Norman V. Loayza The World Bank Luis Servén The World Bank Naotaka Sugawara The World Bank

Abstract: This paper studies the causes and consequences of informality and applies the analysis to countries in Latin America and the Caribbean. It starts with a discussion on the definition and measures of informality, as well as on the reasons why widespread informality should be of great concern. The paper analyzes informality’s main determinants, arguing that informality is not single-caused but results from the combination of poor public services, a burdensome regulatory regime, and weak monitoring and enforcement capacity by the state. This combination is especially explosive when the country suffers from low educational achievement and features demographic pressures and primary production structures. Using cross-country regression analysis, the paper evaluates the empirical relevance of each determinant of informality. It then applies the estimated relationships to most countries in Latin America and the Caribbean in order to assess the country-specific relevance of each proposed mechanism.

*

For valuable comments and suggestions, we are grateful to Ibrahim Elbadawi, Pablo Fajnzylber, Fausto Hernández, Ana María Oviedo, Jamele Rigolini, Jaime Saavedra, and Klaus Schmidt-Hebbel. This chapter draws from Norman V. Loayza and Naotaka Sugawara, ―The Informal Sector in Mexico: Basic Facts and Explanations,‖ El Trimestre Económico, (2009); and Ibrahim Elbadawi and Norman Loayza, ―Informality, Employment, and Economic Development in the Arab World,‖ Journal of Development and Economic Policies, (2008). The views expressed in this chapter are those of the authors and do not necessarily reflect those of the World Bank, their Boards of Directors, or the countries they represent.

Introduction ―Informality‖ is a term used to describe is the collection of firms, workers, and activities that operates outside the legal and regulatory frameworks.1 While informality offers the benefit of avoiding the burdens of taxation and regulation, at the same time, its participants do not get to fully enjoy the protection and services that the law and the state can provide. Informality is sometimes the result of agents ―exiting‖ the formal sector as a consequence of cost-benefit considerations; other times, it is the outcome of agents being ―excluded‖ from formality as this becomes restrictive and the economy segmented. In all cases, informality is a fundamental characteristic of underdevelopment and is best understood as a complex, multifaceted phenomenon. It is determined by both the modes of socioeconomic organization inherent in economies in the transition to modernity and by the relationship that the state establishes with private agents through regulation, monitoring, and the provision of public services. Informality is not only a reflection of underdevelopment; it may also be the source of further economic retardation. It implies the misallocation of resources and entails losing the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets. According to the estimates presented below, there is a great deal of heterogeneity in the types of informality among the countries of Latin America. In all of them, however, informality is much more widespread than in the United States, and some countries in the region are among the most informal economies in the world. The typical country in Latin America produces about 40 percent of its GDP and employs 70 percent of its labor force informally. These are astounding statistics, which indicate that informality is a substantive and pervasive phenomenon that must be explained and addressed, particularly in the design of development policies. This chapter studies informality in Latin America from a macroeconomic and international perspective. It uses cross-country variations on measures and potentially 1

This definition, introduced by De Soto (1989) in his classic study of informality, has gained remarkable popularity due to its conceptual strength, which allows it to focus on the root causes of informality rather than merely its symptoms. For an excellent review of the causes and consequences of the informal sector, see Schneider and Enste (2000). Drawing from a public-choice approach, Gerxhani (2004) provides an interesting discussion of the differences of the informal sector in developed and developing countries. The World Bank report by Perry et al. (2007) is the most comprehensive and in-depth study on informality in the Latin America region.

related variables of informality to study its causes and consequences. It then examines Latin American countries against this broad international context. In this chapter, the next section presents and discusses various measures of informality. This is followed by a section that assesses the impact of informality on economic growth and poverty. Next, we present an analysis of the main causes of informality, followed by a section that evaluates the empirical relevance of each determinant of informality to every Latin American country in the sample. Finally, we offer some concluding remarks.

Measuring Informality in Latin America and Around the World Although the definition of informality can be simple and precise, this is not the case with its measurement. Given that it involves working outside the legal and regulatory frameworks, informality is best described as a latent, unobserved variable, that is, a variable for which accurate and complete measurement is not feasible but for which an approximation is possible through indicators reflecting its various aspects. Here we consider four such indicators, available for a relatively large collection of countries. Two of the indicators refer to overall informal activity in the country, and the other two relate in particular to informal employment. While each indicator on its own has conceptual and statistical shortcomings as a proxy for informality, taken together, they may provide a robust approximation to the subject. The two indicators related to overall informal activity are the Schneider index of the shadow economy and the Heritage Foundation index of informal markets. 2 The Schneider index combines the DYMIMIC (dynamic multiple-indicator-multiple-cause) method, the physical input (electricity) method, and the excess currency-demand approach for the estimation of the share of production that is not declared to tax and regulatory authorities. The Heritage Foundation index is based on subjective perceptions of general compliance with the law, with particular emphasis on the role played by official corruption. The two indicators that focus on the labor aspect of informality are the prevalence of self-employment and the lack of pension coverage. The prevalence of self-employment

2

Details on definitions, sources, and samples for these and other variables used in this chapter are provided in appendix 5.2.

is determined by the ratio of self to total employment, as reported by the International Labor Organization. The lack of pension coverage is estimated through the fraction of the labor force that does not contribute to a retirement pension scheme, as reported in the World Bank’s World Development Indicators. Appendix 5.3 presents some descriptive statistics on the four informality indicators. In particular, it shows that, as expected, they are significantly positively correlated, with correlation coefficients ranging from 0.59 to 0.90—high enough to represent the same phenomenon but not so high as to make them mutually redundant. Using data on these four indicators, we can assess the prevalence of informality across Latin America. For comparison purposes, figure 5.1 presents data on the four informality indicators for individual countries in Latin America and the Caribbean (LAC). The United States and Chile are used as benchmark countries. The United States is the developed country to which the region is most closely related. Chile is the Latin American country often taken as a model for economic reforms and sustained growth in the region.3 It is clear from the figure that there is considerable variation in informality across countries in Latin America. However, in all of them, the degree of informality is much higher than it is in the United States and, for some countries (e.g., Bolivia and Haiti), it is comparable to the most informal countries in the world. For the median country in Latin America, about 40 percent of GDP is produced informally. Informal employment is more difficult to ascertain. Using the measure based on pension contributions, about 70 percent of the labor force is informal in the median country in Latin America.4



The Cost of Informality

3

The LAC countries under consideration are those included in any of the four regressions where informality is a dependent variable (table 5.3). They are 20 countries plus Chile, which functions as a comparator country, unless otherwise noted. Trinidad and Tobago is also excluded since the World Bank classification (as of July 2007) considers the country as a high-income country. See appendix 5.1 for sample of countries in each regression. 4 Self-employment is arguably a lower bound for the measure of informal labor given that tax and regulation evasion occurs massively in all types of firms.

Informality is a distorted, second-best response from an excessively regulated economy to the positive and negative shocks and the growth opportunities that the country faces. It is a distorted response because it implies misallocation of resources and entails losing, at least partially, the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets. Trying to escape the control of the state induces many informal firms to remain suboptimally small, use irregular procurement and distribution channels, and constantly divert resources to mask their activities or bribe officials. Conversely, formal firms are induced to use more intensively the resources that are less burdened by the regulatory regime; especially for developing countries, this means that formal firms are less laborintensive than they should be according to the countries’ endowments. In addition, the informal sector generates a negative externality that compounds its adverse effect on efficiency: informal activities use and congest public infrastructure without contributing the tax revenue to replenish it. Since public infrastructure complements private capital in the process of production, a larger informal sector implies slower productivity growth.5 Compared with a best response, the expansion of the informal sector often represents distorted and deficient economic growth.6 This statement merits further clarification: informality is suboptimal with respect to the best scenario that occurs in an economy without excessive regulations and with adequate provision of public services. Nevertheless, informality is indeed preferable to a fully formal but sclerotic economy that is unable to circumvent its regulation-induced rigidities. This brings to bear an important policy implication: the mechanism of formalization matters enormously for its consequences on employment, efficiency, and growth. If formalization is purely based on enforcement, it will likely lead to unemployment and low growth. If, on the other hand, it is based on improvements in both the regulatory framework and the quality and availability of public services, it will bring about more efficient use of resources and higher growth. 5

See Loayza (1996) for an endogenous-growth model highlighting the negative effect of informality through the congestion of public services. 6 This does not necessarily mean that informal firms are not dynamic or lagging behind their formal counterparts. In fact, in equilibrium the risk-adjusted returns in both sectors should be equalized at the margin. See Maloney (2004) for evidence on the dynamism of Latin American informal firms. The arguments presented in the text apply to the comparison between an excessively regulated economy and one that is not.

From an empirical perspective, the ambiguous impact of formalization highlights an important difficulty in assessing the impact of informality on economic growth: two countries can have the same level of informality, but if the informality has been achieved in different ways, the countries’ growth rates may be markedly different. Countries where informality is kept at bay by drastic enforcement will fare worse than countries where informality is low because of light regulations and appropriate public services. We now present a simple regression analysis of the effect of informality on growth. As suggested above, this analysis must control for enforcement, and a straightforward, albeit debatable, way to do this is by including a proxy for the overall capacity of the state as a control variable in the regression. For this purpose, we try two proxies: the level of GDP per capita, and the ratio of government expenditures to GDP. The former has the advantage of also accounting for conditional convergence, and the latter has the advantage of more closely reflecting the size of the state.7 Another important consideration for this empirical analysis is that informality may not only affect but also be affected by economic growth. For example, faster growth could raise the profitability of production and the real wage, relative to the perceived costs of formality, thus encouraging more firms and workers to shift out of the informal sector. In order to ascertain the impact of informality on growth, we need to isolate the exogenous variation in informality. We do this through an instrumental-variable approach, where the instruments are selected among the variables that are postulated as determinants of informality—indicators of law and order, business regulatory freedom, secondary schooling, and sociodemographic factors. Since some of them have a relationship with economic growth that is independent of informality, we only use as instruments the sets of variables that comply with the exclusion restrictions, as diagnosed by the Hansen test of orthogonality between the instruments and the regression residuals (see notes on tables 5.1a and 5.1b for further explanation). Table 5.1 presents the regression results. The dependent variable is the average growth of per capita GDP over 1985–2005. We choose a period of about 20 years for the measure of average growth in order to achieve a compromise between merely cyclical,

7

We also considered as proxy the ratio of tax revenues to GDP. Even though the number of observations drops considerably, the results were similar regarding the negative effect of informality on growth.

short-run growth (which would be unaffected by informality) and very long-run growth (which could be confused with the sources, rather than consequences, of informality). We consider two alternative control variables: initial GDP per capita (table 5.1a) or initial ratio of government expenditures to GDP (table 5.1b). The explanatory variables of interest are the four informality indicators, considered one at a time. The table first presents the ordinary least-square (OLS) results and then the instrumental-variable (IV) results.

The OLS and IV regression results are basically the same regarding the sign and significance of the coefficients on the informality indicators. If anything, the IV coefficient estimates are somewhat larger in magnitude than their OLS counterparts. They clearly indicate that an increase in informality leads to a decrease in economic growth. All four informality indicators carry negative and highly significant regression coefficients. The harmful effect of informality on growth is not only robust and significant, but its magnitude makes it also economically meaningful: using the estimates from the IV regressions controlling for initial government expenditures/GDP, an increase of one standard deviation in any of the informality indicators leads to a decline of 0.7–1 percentage points in the rate of per capita GDP growth.8 These are conservative estimates when compared to those from the regression that controls for GDP per capita—there, the growth effects of a reduction in informality are about twice as large. There is also a close connection between poverty and informality, reflecting at least in part the negative relationship between economic growth and informality. Table 5.2 presents cross-country regression analysis with the headcount poverty index as dependent variable and, in turn, the four measures of informality as explanatory variables. In order to have a close chronological match between dependent and

8

To be precise, a one-standard-deviation increase of, in turn, the Schneider index, the Heritage Foundation index, the share of self-employment, and the labor force lacking pension coverage leads to a decline of, respectively, 1.1, 0.8, 0.8, and 0.7 percentage points of per capita GDP growth.

explanatory variables, the headcount poverty index corresponds to the latest available measure per country. As in the growth regressions, the level of GDP per capita (table 5.2a) or the ratio of government expenditures to GDP (table 5.2b) are included as control variables. Also as in previous regressions, we present both OLS and IV estimates, the latter to account for the likely endogeneity of informality with respect to poverty.



The regression results reveal a positive relationship between the prevalence of informality and the incidence of poverty. When government expenditure is controlled for, the four measures of informality carry positive and significant coefficients in the IV regressions. Similarly, when the level of GDP per capita is controlled for, three of the four informality indicators carry positive and significant coefficients (self-employment is the exception). The significant relationship between informality, on the one hand, and economic growth and poverty, on the other, is remarkable: it underscores the importance of the issue and urges for analysis on the complex sources of informality. To this, we turn next.

The Causes of Informality Informality is a fundamental characteristic of underdevelopment, shaped by both the modes of socioeconomic organization inherent in economies in the transition to modernity and by the relationship that the state establishes with private agents through regulation, monitoring, and the provision of public services. As such, informality is best understood as a complex, multifaceted phenomenon. Informality arises when the costs of belonging to the country’s legal and regulatory framework exceed its benefits. Formality entails costs of entry—in the form of lengthy, expensive, and complicated registration procedures—and costs of permanence— including payment of taxes, compliance with mandated labor benefits and remunerations,

and observance of environmental, health, and other regulations. The benefits of formality potentially consist of police protection against crime and abuse, recourse to the judicial system for conflict resolution and contract enforcement, access to legal financial institutions for credit provision and risk diversification, and, more generally, the possibility of expanding markets both domestically and internationally. At least in principle, formality also voids the need to pay bribes and prevents penalties and fees, to which informal firms are continuously subject. Therefore, informality is more prevalent when the regulatory framework is burdensome, the quality of government services to formal firms is low, and the state’s monitoring and enforcement power is weak. These cost and benefit considerations are affected by the structural characteristics of underdevelopment, dealing in particular with educational achievement, production structure, and demographic trends. Other things being equal, a higher level of education reduces informality by increasing labor productivity and, therefore, making labor regulations less onerous and formal returns potentially larger. Likewise, a production structure tilted toward primary sectors like agriculture, rather than to the more complex processes of industry, favors informality by making legal protection and contract enforcement less relevant and valuable. Finally, a demographic composition with larger shares of youth or rural populations is likely to increase informality by making monitoring more difficult and expensive, by placing bigger demands on resources for training and acquisition of abilities, by creating bottlenecks in the initial school-to-work transition, and by making more problematic the expansion of formal public services (see Fields 1990; Schneider and Enste 2000; ILO 2004). Popular and even academic discussions often focus on particular sources of informality, rather than taking this comprehensive approach. Thus, some observers stress insufficient enforcement and related government weaknesses such as corruption; others prefer to emphasize the burden of taxes and regulations; yet others concentrate on explanations dealing with social and demographic characteristics. As suggested above, all these possibilities make sense, and there is some evidence to support each of them. To illustrate this, figure 5.2 presents cross-country scatter plots of each of the four measures of informality versus proxies for the major proposed determinants of informality. The sample observations include all countries with available

data; for illustration purposes, countries in Latin America and the Caribbean are highlighted in the figures. The proxies for the determinants of informality are as follows.9 An index on the prevalence of law and order—obtained from The International Country Risk Guide—is used as a proxy for both the quality of formal public services and government’s enforcement strength. An index of business regulatory freedom—taken from Fraser Foundation’s Economic Freedom of the World Report—is used to represent the ease of restrictions imposed by the legal and regulatory frameworks. The average years of secondary schooling of the adult population—taken from Barro and Lee (2001)—represents the educational and skill achievement of the working force. And, finally, an index of sociodemographic factors—constructed from the World Bank’s World Development Indicators and other databases—which includes the share of youth in the population, the share of rural population, and the share of agriculture in GDP, was selected.10



Remarkably, all 16 correlation coefficients (4 informality measures times 4 determinants) are highly statistically significant, with p-values below 1 percent, and of large magnitude, ranging approximately between 0.54 and 0.87. All informality measures present the same pattern of correlations: informality is negatively related to law and order, regulatory freedom, and schooling achievement, and it is positively related to factors that denote the early stages of sociodemographic transformation. Therefore, all of these explanations may have some truth in them. What we need to determine now is whether each of them has independent explanatory power with respect to informality. More specifically, we need to assess to what extent each of them is relevant both in general for the cross-section of countries and in particular for a given country. We turn next to this purpose.

9

Again, details on definitions and sources of all variables are presented in appendix 5.2. This is constructed by first standardizing each component (to a mean of zero and a standard deviation of 1) and then taking a simple arithmetic average. We use a composite index, rather than the components separately, given the very high correlation among them. 10

In what follows, we use cross-country regression analysis to evaluate the importance of each explanation regarding the origins of informality. Each of the four informality measures presented earlier serves as the dependent variable of its respective regression model. The set of explanatory variables is common to all informality measures and represents the major determinants of informality. They are the same variables used in the simple correlation analysis, introduced above. Then we apply these estimated relationships to the case of the Latin American and Caribbean countries with available data in order to evaluate the country-specific relevance of each proposed mechanism. We can do this for those countries that possess complete information on dependent and explanatory variables, or at least information on the latter, with which we can obtain predicted values of the dependent variable. There are 20 countries in the Latin American and Caribbean region that possess complete information on all explanatory variables, but comparable data on self-employment and pension coverage are not available for Haiti. Likewise, Nicaragua and Paraguay do not have data on self-employment, and Guyana has data on the Heritage index only. (In both cases, however, we can construct for them a predicted value based on the regression analysis using the sample of all other countries.) The regression results are presented in table 5.3. They are remarkably robust across informality measures. Moreover, all regression coefficients have the expected sign and are highly significant. Informality decreases when law and order, business regulatory freedom, or schooling achievement rise. Similarly, informality decreases when the production structure shifts away from agriculture and when demographic pressures from youth and rural populations decline. The fact that each explanatory variable retains its sign and significance after controlling for the rest indicates that no single determinant is sufficient to explain informality. All of them should be taken into account for a complete understanding of informality.



The four explanatory variables account jointly for a large share of the crosscountry variation in informality: the R-squared coefficients are 0.57 for the Schneider shadow economy index, 0.89 for the Heritage Foundation informal market index, 0.78 for

the share of self-employment, and 0.88 for the share of the labor force not contributing to a pension program.

Explaining Informality in Latin American Countries The cross-country regression analysis presented above can be used to assess the determinants of informality that are most relevant to each Latin American country. The first issue to explore is whether these countries are outliers or follow the general trend established by the cross-country regressions. Figure 5.3 presents a scatter plot of the actual vs. predicted values of each informality measure. (For illustrative purposes, observations corresponding to Latin American countries are highlighted in the figure). The majority of countries in the world have small residuals (i.e., the unpredicted portion of informality), a fact that is consistent with the large R-squared coefficients obtained in the regressions.



Is this also the case for the Latin American and Caribbean countries under consideration? The answer is not simple and must be nuanced by the heterogeneity of the countries in the region. Some Latin American and Caribbean (LAC) countries are located around the 45-degree line, but others are quite far from it. In fact, when we include a ―Latin American and Caribbean country‖ dummy in the regressions, its coefficient turns out to be positive in all cases and significant in three of them (the exception is selfemployment).11 The significance of the regional dummy indicates that the actual values of informality are larger than the predicted values for the majority of countries in the region. This is so for the Heritage index and the pension coverage measure. For the Schneider index, not only do the majority of countries have positive residuals but some of them also could be considered as outliers. In terms of specific countries, the following points seem noteworthy. For Brazil, Costa Rica, Honduras, and Jamaica, the predicted values of informality are similar to 11

Regression results with ―LAC country‖ dummy are not presented but are available upon request. For the Schneider index, the Heritage index, self employment, and pension coverage, t-statistics of the dummy variable are 2.91, 2.46, 1.20, and 2.36, respectively.

their actual counterparts in all of the four informality measures. Five more countries— Argentina, Guatemala, Nicaragua, Panama, and Uruguay—join this group in all but the Schneider index. In Colombia and the Dominican Republic, while predicted values are much smaller than actual ones regarding labor informality (the last two indices), the actual and predicted values of production informality (that is, the first two indices) are quite close. Contrary to this, as clearly shown in the figure, actual values of the Schneider index are much larger than predicted ones for Bolivia, Panama, Peru, and Uruguay, which in part explains why the R-squared coefficient for this regression is smaller than those of the other informality measures. Focusing now on the portion of informality explained by the cross-country regression model, we can evaluate the importance of each explanatory variable for the case of the 20 Latin American and Caribbean countries with sufficient available data. In particular, we can assess how each determinant contributes to the difference in informality between individual countries and a comparator one, for which we choose Chile, given its widely-recognized status of reform leader in the region. The contribution of each explanatory variable is obtained by multiplying the corresponding regression coefficient (from table 5.3) times the difference in the value of this explanatory variable between each Latin American and Caribbean country and the comparator country. The importance of a particular explanatory variable would, therefore, depend on the size of its effect on informality in the cross-section of countries and how far apart the two countries are with respect to the explanatory variable in question. Naturally, the sum of the contributions equals the total difference in predicted informality between each individual country and Chile. This difference is plotted in figure 5.4. As expected, it shows that all the countries have larger (predicted) informality levels than Chile. Haiti, Honduras, and Guatemala are predicted to be the most informal (and in general show the largest difference with respect to Chile). On the other hand, Uruguay, Argentina, and Costa Rica are predicted to be the least informal among the Latin American and Caribbean countries, though they still show larger informality levels than Chile.



Figure 5.5 presents the decomposition of the difference of (predicted) informality between each of the 20 countries under analysis and Chile. The figure has four panels, corresponding to each of the four informality indicators. The most remarkable observations are the following. Policy and institutional variables related to the quality of the state are the most important factors explaining the differences in informality. Restricted regulatory freedom tends to contribute to larger informality in all Latin American and Caribbean countries for the Heritage index, self employment, and pension coverage, while deficient law and order explains the bulk of informality for the Schneider index.



Education, measured by average years of secondary schooling, does not play a major role in explaining differences in informality with respect to Chile for any of four informality measures, even in the cases of Haiti and Honduras. Sociodemographic factors are particularly important in explaining the differences regarding labor informality, and less so regarding production informality. Moreover, in the case of labor informality, the larger the differences in informality with respect to Chile, the larger the importance of sociodemographic factors. This is the case, for instance, of Haiti and Honduras, where all determinants of informality (excluding educational level) are about as important. On the other hand, there is no such trend regarding the two production informality measures: for them, the variables dealing with the quality of the state are always more important,

especially law and order for the Schneider index and regulatory freedom for the Heritage index.

Conclusion By any measure, informality is quite prevalent in the countries of Latin America and the Caribbean. This is worrisome because it denotes misallocation of resources (labor in particular) and inefficient utilization of government services, which can jeopardize the countries’ growth and poverty-alleviation prospects. The evidence presented in this chapter shows that informality has a statistically and economically significant negative impact on growth—and an equally significant positive impact on the incidence of poverty across countries. Informality arises when the costs of belonging to the economy’s legal and regulatory framework exceed the benefits. Thus, informality is more prevalent where the regulatory framework is burdensome, the quality of government services is low, and the state’s monitoring and enforcement capacity is weak. However, these cost-benefit calculations are also affected by key structural characteristics of the economy, such as its productive and demographic structure and the availability of skilled labor. This chapter has argued that it is important to take into account all of these factors when trying to ascertain the causes of informality. In the case of Latin America, this chapter has shown that informality is primarily the outcome of a combination of poor public services and a burdensome regulatory framework. Low levels of education, as measured by secondary schooling, are less important in this respect. In lower income countries, informality (particularly regarding labor markets) is exacerbated when the production structure is heavily based on agriculture and other rural activities and when the labor participation of young people, resulting from recent demographic transition, is large. Informality is a complex phenomenon that is best understood from several angles, considering different indicators that reflect its various aspects and treating it as both cause and consequence of underdevelopment. This chapter is a modest contribution in this direction.

References Barro, Robert, and Jong-Wha Lee. 1993. ―International Comparisons of Educational Attainment.‖ Journal of Monetary Economics 32(3): 363–94. ———. 2001. ―International Data on Educational Attainment: Updates and Implications.‖ Oxford Economic Papers 53(3): 541–63. De Soto, Hernando. 1989. The Other Path: The Invisible Revolution in the Third World.New York, NY: HarperCollins. Fields, Gary. 1990. ―Labour Market Modeling and the Urban Informal Sector: Theory and Evidence.‖ In The Informal Sector Revisited, ed. David Turnham, Bernard Salomé, and Antoine Schwarz. Paris: OECD, 49–69. Gerxhani, Klarita. 2004. ―The Informal Sector in Developed and Less Developed Countries: A Literature Survey.‖ Public Choice 120(3/4): 267–300. Gwartney, James, Robert Lawson, Russell Sobel, and Peter Leeson. 2007. Economic Freedom of the World: 2007 Annual Report. Vancouver, BC: The Fraser Institute. Available at www.freetheworld.com. ILO (International Labour Organization). 2004. Global Employment Trends for Youth. Geneva: ILO. ILO. 2007. Yearbook of Labour Statistics. Geneva: ILO. Available at LABORSTA Internet, laborsta.ilo.org. Loayza, Norman. 1996. ―The Economics of the Informal Sector: A Simple Model and Some Empirical Evidence from Latin America.‖ Carnegie-Rochester Conference Series on Public Policy 45: 129–62. Loayza, Norman, and Claudio Raddatz. 2006. ―The Composition of Growth Matters for Poverty Alleviation.‖ Policy Research Working Paper 4077. Washington, DC: The World Bank. Loayza, Norman, and Jamele Rigolini. 2006. ―Informality Trends and Cycles.‖ Policy Research Working Paper 4078. Washington, DC: The World Bank. Maloney, William. ―Informality Revisited.‖ 2004. World Development 32(7): 1159–78.

Miles, Marc, Edwin Feulner, and Mary O’Grady. 2005. 2005 Index of Economic Freedom. Washington, DC: Heritage Foundation. Perry, Guillermo, William Maloney, Omar Arias, Pablo Fajnzylber, Andrew Mason, and Jaime Saavedra-Chanduvi. 2007. Informality: Exit and Exclusion. Washington, DC: The World Bank. PRS Group. 2007. International Country Risk Guide (ICRG). Syracuse, NY: PRS Group. Available at www.icrgonline.com. Schneider, Friedrich. 2004. ―The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003.‖ IZA Discussion Paper 1431. Bonn: Institute for the Study of Labor. Schneider, Friedrich, and Dominik Enste. 2000. ―Shadow Economies: Size, Causes, and Consequences.‖ Journal of Economic Literature 38(1): 77–114. United Nations (UN). 2005. World Population Prospects: The 2004 Revision. CD-ROM edition. New York, NY: UN. World Bank. 2006. World Development Indicators 2006. Washington, DC: The World Bank. World Bank. 2007. World Development Indicators 2007. Washington, DC: The World Bank.

Table 5.1a: The Effect of Informality on Economic Growth, Controlling for GDP Per Capita Dependent variable: Per capita GDP Growth, 1985–2005, country average OLS

Initial GDP per capita (2000 US$, 1985, in logs)

Schneider Shadow Economy (% of GDP)

IV

[1]

[2]

[3]

[4]

-0.1966 1.29

-0.3519 1.54

-0.3498* 1.88

-0.6910* 1.98

-0.0747*** 3.87

Heritage Foundation Informal Market

[6]

[7]

-0.1479*** 4.39

Self Employment

-1.3294*** 4.05 -0.0657*** 3.11

(% of total employment)

Non-contributor to Pension Scheme

-0.1775*** 3.21 -0.0423*** 2.80

(% of labor force)

5.4231*** 3.15

[8]

-0.6976*** -0.7684*** -1.2819*** -1.7200*** 3.06 2.83 2.69 2.95

-0.8009** 2.41

(ranging 1-5: higher, more informality)

Constant

[5]

6.9131** 2.57

6.6475*** 3.35

9.2161** 2.59

-0.0872*** 3.39 11.8634*** 11.7604*** 17.1971*** 19.8890*** 4.29 3.80 3.18 3.33

No. of observations

119

127

72

91

84

87

59

R-squared

0.20

0.08

0.13

0.11

-

-

-

-

-

-

-

-

0.48

0.21

0.30

0.70

Hansen J Statistic (P-value)

68

Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively. 3. For IV regressions [5] to [8],  Endogenous variable: each of four informality measures.  Instruments: Law and order; Business regulatory freedom; Average years of secondary schooling.  Sociodemographic factors are not included as an instrument because they do not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic). 4. See appendix 5.2 for definitions and sources of variables. Source: Authors’ estimation.

Table 5.1b: The Effect of Informality on Economic Growth, Ccontrolling for Government Expenditure/GDP Dependent variable: Per capita GDP Growth, 1985–2005, country average OLS [1]

Initial Government Expenditure

-0.0340* 1.96

(% of GDP, 1985)

Schneider Shadow Economy (% of GDP)

[2]

IV [3]

-0.0513** -0.0681*** -0.0588** 2.60 2.82 2.59

-0.0622*** 4.76

Heritage Foundation Informal Market

[5]

[6]

[7]

-0.0789*** 4.31

Self Employment

-0.6085*** 4.18 -0.0557*** 3.84

(% of total employment)

Non-contributor to Pension Scheme

-0.0596*** 2.85 -0.0183*** 3.58

(% of labor force)

4.1214*** 6.71

[8]

-0.0593** -0.0717*** -0.1008** -0.0776*** 2.14 2.94 2.55 3.34

-0.6724*** 5.52

(ranging 1-5: higher, more informality)

Constant

[4]

4.5441*** 7.98

4.6023*** 6.69

-0.0203*** 3.51

3.5267*** 6.19

5.0933*** 6.18

4.6934*** 6.72

5.0909*** 4.50

4.1156*** 6.70 72

No. of observations

112

118

69

85

88

91

59

R-squared

0.20

0.18

0.18

0.11

-

-

-

-

-

-

-

-

0.53

0.89

0.62

0.72

Hansen J Statistic (P-value)

Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively. 3. For IV regressions [5] to [8],  Endogenous variable: each of four informality measures.  Instruments: Business regulatory freedom; Average years of secondary schooling; Sociodemographic factors.  Law and order is not included as an instrument because it does not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic). 4. See appendix 5.2 for definitions and sources of variables. Source: Authors’ estimation.

Table 5.2a: The Effect of Informality on Poverty, Controlling for GDP Per Capita Dependent variable: Poverty Headcount index, latest year OLS [1]

Initial GDP per capita (2000 US$, 1985, in logs)

Schneider Shadow Economy (% of GDP)

[2]

IV [3]

-0.1331*** -0.1028*** -0.0995*** -0.0656** 6.18 4.07 3.02 2.33 0.0067** 2.34

Heritage Foundation Informal Market

[5]

[6]

-0.1129*** -0.0800*** 3.48 3.10

[7]

[8]

-0.0796 1.26

-0.0346 0.94

0.0104* 1.71 0.0841** 2.38

(ranging 1-5: higher, more informality)

Self Employment

0.1229* 1.89 0.0004 0.22

(% of total employment)

Non-contributor to Pension Scheme

-0.0017 0.24 0.0031** 2.34

(% of labor force)

Constant

[4]

0.8607*** 4.54

No. of observations R-squared Hansen J Statistic (P-value)

0.6053** 2.30

0.8476*** 3.48

0.0051** 2.08

0.4127 1.55

0.5717 1.46

0.3001 0.83

0.7636 1.09

0.0436 0.11 38

51

51

34

46

41

42

30

0.51

0.42

0.34

0.35

-

-

-

-

-

-

-

-

0.47

0.33

0.11

0.69

Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively. 3. For IV regressions [5] to [8],  Endogenous variable: each of four informality measures.  Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average years of secondary schooling; Sociodemographic factors). 4. See appendix 5.2 for definitions and sources of variables. Source: Authors’ estimation.

Table 5.2b: The Effect of Informality on Poverty, Controlling for Government Expenditure/GDP Dependent variable: Poverty Headcount index, latest year OLS

Initial Government Expenditure (% of GDP, 1985)

Schneider Shadow Economy (% of GDP)

IV

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

0.0031 0.51

0.0096 1.58

0.0114 1.09

0.0063 1.01

0.0033 0.37

0.0157* 1.86

0.0224*** 3.44

0.0123 1.54

0.0075* 1.95

Heritage Foundation Informal Market

0.0240*** 2.97 0.2135*** 4.41

(ranging 1-5: higher, more informality)

Self Employment

0.2470*** 3.47 0.0091 1.51

(% of total employment)

Non-contributor to Pension Scheme (% of labor force)

Constant No. of observations R-squared Hansen J Statistic (P-value)

-0.1130 0.59

-0.7019*** 3.33

-0.2911 0.99

0.0230*** 3.08 0.0064*** 4.95

0.0076*** 3.41

-0.3624*** 2.86

-0.7887** -0.9201*** -0.9325*** -0.5467** 2.45 2.77 3.34 2.56

48

48

32

43

40

41

29

0.12

0.33

0.13

0.36

-

-

-

-

-

-

-

-

0.25

0.14

0.52

0.61

Notes: 1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively. 3. For IV regressions [5] to [8],  Endogenous variable: each of four informality measures.  Instruments: four determinants of informality (Law and order; Business regulatory freedom; Average years of secondary schooling; Sociodemographic factors). 4. See appendix 5.2 for definitions and sources of variables. Source: Authors’ estimation.

37

Table 5.3: Determinants of Informality Method of estimation: Ordinary Least Squares with Robust Standard Errors Dependent variable: Four types of informality measures, country average

Explanatory variables: Average of 2000-2005 by country

Schneider Shadow Economy index (% of GDP) [1]

Law and Order (ICRG, index ranging 0-6: higher, better)

Business Regulatory Freedom (The Fraser Institute, index ranging 0-10: higher, less regulated)

Average Years of Secondary Schooling (Barro and Lee 2001)

Sociodemographic Factors (average of share of youth population, share of rural population, and share of agriculture in GDP)

Constant

No. of observations Adjusted R-squared

Informality measures Heritage Foundation Self Non-contributor to Informal Market index Employment Pension Scheme (1-5: higher, more) (% of total employment) (% of labor force) [2] [3] [4]

-3.2360** -2.57

-0.0969* -1.76

-1.6925* -1.84

-2.9764* -1.67

-2.0074* -1.80

-0.5333*** -9.95

-2.5196** -2.17

-5.8675** -2.28

-1.9684* -1.70

-0.1152** -2.00

-2.1527** -2.25

-5.8114*** -3.27

3.8438** 2.00

0.5027*** 4.99

5.9743*** 3.77

21.6130*** 7.31

60.3429*** 10.48

6.6326*** 31.72

54.7254*** 14.06

113.3110*** 11.40

84 0.57

86 0.89

57 0.78

70 0.88

Notes: 1. t-statistics are presented below the corresponding coefficients. 2. *, **, and *** denote significance at the 10 percent, 5 percent, and 1 percent levels, respectively. 3. See appendix 5.1 for countries included in each regression and appendix 5.2 for definitions and sources of variables and periods used to compute country averages of informality measures. Source: Authors’ estimation.

Figure 5.1: Size of Informality, Various Measures A. Schneider Shadow Economy index 80

(% of GDP)

60

LAC Countries

Comparator 40 Countries

20

0

USA CHL

CRI ARG P RY MEX DOM ECU VEN JAM BRA COL NIC SLV HND URY GTM HTI PER P AN BOL

B. Heritage Foundation Informal Market index 5

(Higher, more informality)

4

LAC Countries

Comparator Countries

3

2

1

USA CHL URY CRI JAM MEX SLV P AN ARG BRA DOM PER COL GTM ECU GUY HND BOL NIC VEN HTI PRY

C. Self Employment (% of total employment) 40

LAC Countries Comparator 30 Countries

20

10

0

USA CHL

ARG CRI URY MEX BRA PAN SLV VEN ECU JAM GTM PER HND COL BOL DOM

D. Non-contributor to Pension Scheme (% of labor force) 80

LAC Countries Comparator 60 Countries 40

20

0

USA CHL URY CRI PAN JAM BRA ARG MEX ECU VEN DOM PER SLV GTM HND P RY COL NIC BOL

Figure 5.2: Informality and Basic Determinants A. Schneider Shadow Economy index (% of GDP) 80

(% of GDP)

ZWE

60 ZAR

40 COL

20

NGA HTI

BOL PAN PER

80

correlation: -0.62*** N = 118 AZE

HTI

correlation: -0.60*** N = 125

GEO BOL PAN AZE TZA PER NGA

UKR

THA GTM URY HNDBENZMB ARM MDA SLV RUS TCD NIC LKA CAFSEN PHL UGA KAZ MLI SLE CIV NER GHA BFA MOZ C OL ETH MWI MDG BRA LVA RWATUN EST KGZ TGO NPL PAK BDI PNG BGR JAM MRT MAR BGD ROM EGY BIH MKD VEN DZA KEN ECU ALB TUR HRV FJI CMR LSO BWA NAM MEXDOM MYS LTU ZAF POLKOR GRCSVN ARGPRY ARE TWN CRI ITA HUN IND IDN PRT ESP ISR KWT JOR SYR CHL MNG CZE BEL SVK IRN OMN NOR SWE FIN DNK VNM CAN HKG CHN FRA DEUIRLAUS SGP NZL GBR JPNNLD AUT CHE USA

COG ZAR AGO

40

20

0 1

2

3

4

5

6

2

Law and Order (index: higher, better) (% of GDP)

80

PAN PER

ZWE

TZA

4

5

6

7

8

(% of GDP) GEO BOL PAN PER AZE

ZWE TZA NGA HTI THA KHM GTM HND ZMB COG ZAR ARM MDANIC SLV SEN BENTCD CAF LKA PHL UGA CIV GHASLE KAZ AGO MLI NER BFA MOZ COL ETH RWA MWI GIN LVA MDG KGZ NPL TGO ESTBRAYUGTUN BDI PNG BGR ROM MRT PAK MAREGY BGDUZB TON BIHJAM LBN MKD KEN VEN ECU DZA HRV FJI ALB BWA CMRLSO TUR DOM NAM WSM LTUMEX MYS BTN LAO PRI KOR PRYVUT ZAF SVN YEM POL GRC ARE ARG CRI ITA HUN IND ESP PRT IDN BEL KWT JOR MNGSYR CZECHL SVK OMN IRN SAU SWE NOR FIN DNK VNM HKG DEU CAN IRL CHN FRA AUS SGP NLD NZL GBR JPN AUT CHE correlation: 0.54*** USA

60

HTI THA GTM URY ZMB COG ZAR HND BEN SEN SLV NIC LKA CAF UGA PHL MLI NER SLE GHA MOZ RWA COL MWI BRA TGONPL TUN 40 PAKJAM PNG MRT MAR BGD EGY KEN ECU VEN FJI DZA BWA TUR LSOCMR DOM MYS MEX PRY ARG ZAF YEM GRC TWN ARE CRI POL ITA HUN IND

UKR URY BLR RUS

40 KOR

ISR ESP IDNSYR KWT JOR BEL CHLIRN PRT NOR SWE FINHKG DNK DEU CAN CHNSGP FRAIRL AUS NLD NZL GBR JPN AUTCHE USA

20

3

Business Regulatory Freedom (index: higher, less regulated)

correlation: -0.66*** N = 94

BOL 60

ZWE

60

TZA

UKR THA GTM URYARM HND ZMB BLR COG MDA RUS NIC SLV SEN LKA PHL UGA CIV AGO MLI SLE NER GHA BFA KAZ ETH GIN MOZMWI BRA MDG LVA TGO EST TUN PAK YUG PNG JAM BGR MAR BGD KEN ROM EGY DZA VEN ECU ALB HRV CMR BWALBN TUR DOM NAM MEX MYS LTU ZAFPRY SVN YEM POL KOR GRC ARG ARE TWN CRI HUN ITA IND ISR IDN ESP PRT BEL KWT JOR SYR CHL MNG CZE SVK IRN OMN SAU NOR SWE FIN DNK VNM HKG DEU CAN IRL CHN FRA AUS SGP GBR NLD NZL JPN CHE AUT USA

0

80

(% of GDP)

20

N = 137 0

0 0

1

2

3

4

5

-2

-1

Average Years of Secondary Schooling

0

1

2

Sociodemographic Factors, standardized

B. Heritage Foundation Informal Market index (range 1–5: higher, more informality) (Higher, more informality)

(Higher, more informality)

5

GNB IRQNER TGO SUR YUG CUB PRK MMR BGD COG YEMPRY MLI LBN LBYIRN GMB SYR HTI SLE ALBIDN AZE CMR NGA KEN MOZ PAK BFANIC KAZ BLR IND VNM VEN BOL ZWE CIV GINECU ARM RUS ROM UKR UGA ZMB MDA TZA 4 HND PHLMDG GUY MWI ETH COL GTM SEN EGY CHN LVA MLT BRA DOMPAN PER ARG GAB BGR POL SVK TUR HRV CZE MEX SLV ZAF DZA LKA THA LTU JOR MAR JAMGHA PNG GRC MNG 3 CRI URY TTO MYS BWAHUN KORSVNTUN NAM SAU ISR EST ITA CYP TWN KWT BHR QAT BHS ESP OMN PRT 2 FRA JPN BEL CHL ARE IRL correlation: -0.69*** HKG DEU USA AUT N = 134 GBR DNK CHESGP NZL AUS CAN NOR SWE NLD LUX FIN ISL 1

1

2

3

4

5

5

HTI

4

3

2

1

6

3

Law and Order (index: higher, better) (Higher, more informality) RWA NER MMR TGO IRQ NPL 5 GNB MLIYEM BGD GMB SLE HTI CMR KEN

4

3

2

1

MOZ IND PAK BOL NIC VEN TZAMRT BEN UGA LSO CAFSWZ ZMB FJI ZWE HND GUY ECUPHL MWI GTM COL SEN CHN BRA DOM EGY ARG PER PAN TUR POL SLV MEX GHA THA ZAF MARDZA JAMJOR LKA PNG CRI URYGRC TUN MUS TTO KOR BWA MYS HUN ISR ITACYP BHR PRT ESP TWNBRB KWT JPN BEL FRA CHL ARE IRL HKG AUT USA DEU GBR DNK SGP ISL NZL NLD AUS FIN CAN CHE NOR SWE

1

2

3

4

Average Years of Secondary Schooling

5

6

7

8

(Higher, more informality) 5

SUR YUG IRQ GNQ TKM TGO TCDNER NPL LAO GNB RWA LBN BIH GEO COG IRN GMB YEM BGD TJKMLI MMR PRY SYR HTISLE AZE IDNALB CMR NGA KEN MKD BLR KAZBOL NIC IND PAK MOZ VNM UZB BFA VEN MDG RUSUKR DJI ECU ROM ARM PHL MDA FJI CPV MRTCIV GIN ZMB ZWE BEN KGZ SWZ LSOUGA TZA CAF HND GUY MWIETH COL GTM LVABRAPER CHN EGYSEN ARG DOM PAN CZE BGR HRV POL SVK GAB TUR MEX SLV LTU JOR JAM ZAFTHA DZA MAR LKA GHA KHM GRC BLZ MNG PNG CRI URY KOR MUS TUNBWA TTO MYS NAM HUNSVNSAU ITA EST KWT ESP PRT BRB OMN WSM FRA BEL JPN CHL correlation: 0.72*** HKGDEUUSA AUT AREIRL N = 149 DNK GBR SGP LUX SWE NLD CHE NOR AUS CAN ISL NZL FIN

4

3

2

1 0

4

Business Regulatory Freedom (index: higher, less regulated)

correlation: -0.80*** N = 105

COG IRN PRY IDNSYR

TCDNER TGO NPL RWA BGD MLI IRN PRY GEO BIH SYRSLE AZE ALB CMR IDNNGA KEN MKD PAK MOZ VNM BOL BFA IND KAZ VEN NIC MDG ZWE UKR RUS MRT CAF LSO PHLBEN ROM ZMB UGA TZA ARM KGZ MDA CIV FJI HND ECU GUY MWI ETH GTM COL SEN CHN EGY MLT LVA ARG PAN BRA DOM PER BGR POL HRV TUR SVK CZE MEX SLV DZA MARLKA GHA THA JOR LTU ZAF JAM GRC MNG PNG CRI URY MUS KOR TTO TUN BWA NAM MYS SVN ISR HUN ITACYP EST TWN BHRBRB KWT PRT ESP OMN JPN BELFRA CHL ARE IRL DEU AUT USA HKG correlation: -0.79*** GBR DNK N = 131 NLD LUXNOR CHE AUS CAN SWE NZL SGP ISL FIN COG

5

-2

-1

0

1

Sociodemographic Factors, standardized

2

Figure 5.2: Informality and Basic Determinants (continued) C. Self employment (% of total employment) (% of total employment) 60

40

COL

IDN

MDG DOMBOL BGD HND PAK PER GTM PHL JAM ECU YEM VEN LKAGRC PAN BRADZA SLV MEXURY ARG

20

(% of total employment)

correlation: -0.72*** N = 69

CMR

ZAF

TTO MYS

2

3

VNM ZMB IRN THA MNG EGY KOR

CRI

TWNITA

60

ETH SYR

VEN MAR NAM

NZL IRL ISL CAN MLTCHESGP AUS BEL GBR HKG OMN ISR AUT NLD JPN DEU FRA DNK USA NOR

20

ESP

QAT

0 1

4

0

5

6

3

Law and Order (index: higher, better) (% of total employment) 60

IDN MDG BOL DOM ETH COL VNM BGDPAK ZMB HND PER GTM PHL JAM IRN THA SYR ECU MNG SLV LKA GRC PAN BRA DZA EGY CHL MAR KORNAM MEX URY CRI FJI ARG TUN PRT ITA TWN TTO MYS CYP NZL ZAF ESP MUS IRL CAN CHE SGP ISL MLT BRB BEL AUS GBR ISR OMN HKG AUT JPNNLD DEU FRA DNK USA NOR LSO

40

CHL TUN PRT CYP

4

5

6

7

8

Business Regulatory Freedom (index: higher, less regulated)

correlation: -0.67*** N = 65

CMR

correlation: -0.70*** N = 71

CMR

correlation: 0.71*** N = 74

60

CMR

(% of total employment) IDN BOLDOM COL BGD HND ZMB PAK PER GTM JAM IRNPHL THA SYRECU YEM VEN SLV LKA GRC PAN BRA DZA EGYMEX MARCHL URY CRI FJI ARG TUN PRT

40

20

ZAF

LSO

IDN

MDG DOM BOL COL VNM BGD ZMB PAK PER PHL HND JAM IRN GTM THA SYR YEM VEN ECU MNG LCA LKA GRC BRA PAN SLVCPV BLZ CHL MEX WBGDZA MAREGY KOR URY NAM CRI FJI ARG

40

KOR

ITATWN TTOCYP MYS NZL MUS IRL ESP CAN CHE SGP BEL ISL AUS BRB GBR NLD ISR HKGAUT JPN DEU FRA DNK USA NOR

0

TUN

PRT

ITA

20

ETH KHM

TTO

MYS NZL ZAF MUS ESP ISL IRL CAN SGP CHE PRI BRB BEL AUS GBR HKG OMN AUT NLD JPN DEU FRA DNK USA NOR

LSO

0 0

1

2

3

4

5

-2

-1

Average Years of Secondary Schooling

0

1

2

Sociodemographic Factors, standardized

D. Non-contributor to pension scheme (% of labor force) (% of labor force)

100

80 COL

GIN MOZ BGD NGA MDG SLE SEN PAK SDN CIV GHAIDN ZWE KEN BOL CMR YEM GAB TGO IRQ

HND GTM PRY SLV PER DOM PHL VEN MEX ECU DZA

NIC CHN THA

ALB

BRA JAM

ARG YUG MYS

PAN URY

40

ARM

20

GRC

0 1

2

4

(% of labor force) 100 MOZ TZA BGD SLE NPL BEN SEN MRT UGA PAK SDN ZMB GHA IDN

5

80 60 40

POL HUN

20

UKR

0 3

4

100

1

2

5

7

8

SLV DOM VEN PER PHL LBN MEXECUDZAIRN JOR BIH KAZ TUR

LKA ALB

KGZ

ARG BRAYUG JAM MYS GEO AZE EGY PAN CRI TUN MUS ROM MKD URY MDAMNG CHL

BGR SVK HRV UKR KOR LVA POL HUNLTU EST CZEGRC SVN BEL ITA AUT PRT ESP FRA SWE DNK DEU GBR USAFIN IRL AUS NLD NOR JPNNZL CHE

ARM

SGP

AUT

DEU USA SWE NOR CHE

4

6

GIN NGA MOZ TZA NPL BFA BGD SEN BEN SLE MRT PAK UGA BDI MDG IDN ZMB SDN GHA VNM IND CIV RWA ZWE KEN GAB BOL NIC YEMCMR TGO THA IRQ WBG CHN MAR COL PRY HND GTM

40

KOR

3

KAZ

(% of labor force)

20

0 0

SLV

Business Regulatory Freedom (index: higher, less regulated)

60

SGP

correlation: -0.70*** N = 101

TUN CHL BGR ARM SVK HRV SGP KOR LVA POL HUN LTU GRCCZE EST SVN BEL PRT ESP AUTDNKSWE ITA FRA DEU FIN GBR AUS USA NLD IRL NOR JPN NZL CHE

80

GRC BEL PRT ESP ITA FRA DNK GBR NLD AUS FIN IRL NZL JPN

0

ARG BRA JAM MYS GEO AZE EGY PAN MUS CRI ROM MKD URY MDA MNG

20

6

correlation: -0.84*** N = 78

IND RWA ZWE BOL CMR YEMKEN TGO NIC THAMAR CHN IRQ COL PER PRY HND GTM SLV DOM VENPHL ECU IRNMEX DZA JOR LKA BRA TUR ARG JAM MYS PAN EGY CRI TUN MUS URY CHL

BIH KGZ ALB TUR

40

Law and Order (index: higher, better)

CIV RWA

COL HND PRY GTM PER DOM PHL ECU MEX LKAIRN DZA JOR

VEN

60

AZE EGY CRI TUN ROM MDA CHL MNG BGR LBY SVK HRV UKR KOR SGP LVA POL HUN LTU EST SVN CZE BEL PRT AUT ITA FRA ESP FIN SWE DNK DEU USA GBR AUS NLD NOR IRL JPN NZL CHE

3

NGA MOZBDI TZA NPL BGD BFA SLE BENUGA MRT PAKSEN MDG IDN ZMB GHA VNM IND ZWE KEN BOL CMR TGO NIC CHN MAR THA

80

MAR

LBNIRN JOR KAZ TUR

LKA

60

(% of labor force)

100

correlation: TZA -0.72*** N = 99

BFA UGA ZMB VNM IND

5

Average Years of Secondary Schooling

Note: *** denotes significance at the 1 percent level.

-2

-1

correlation: 0.87*** N = 109 0

1

Sociodemographic Factors, standardized

2

Figure 5.3: Predicted and Actual Levels of Informality A. Schneider Shadow Economy index 70

Actual values (% of GDP)

60

ZWE

HTI THA GTM URY ZMB COG HND SLV SEN NIC LKAPHL UGA MLI SLE GHA MOZ NER COL MWI BRA TGO TUN PAK PNG BGD MAR EGYJAM ECU VEN DZA KEN CMR BWA TUR DOM MYSMEX PRY ZAF POL KOR ARE GRC ARG CRI ITA HUN IND

50 40 30

10

3 2

Predicted values (% of GDP)

0

10

20

30

40

NER COG MLIBGD HTI IDN KEN CMR IND MOZ BOL NIC VENPAK PHL ZMB UGA TZA ECU GUY HND ZWE COL GTM MWI CHNEGY SEN PER ARG BRA DOM PAN POL TUR SLV MEX JORZAF JAM THA MAR LKA DZA GHA GRC CRI PNG URY KOR TUN MYS TTO BWA HUN ITA KWT ESP PRT JPN FRA BEL CHL IRL ARE HKG USA DEU AUT DNKGBR FIN SWE ISL SGPNZL CAN NOR CHE AUS NLD

4

0 50

60

1

Actual values (% of total employment)

IRN SYR

70

1

2

3

Actual values (% of labor force)

ITA

20

NZL IRL ESP ISL CAN CHE SGP BEL AUS GBR HKG AUT NLDJPN DEU FRA DNK USA NOR

10

0

10

20

JOR

60 CHLURY

40 SGP

KOR

20 Predicted values (% of total employment)

30

40

50

60

0

ZWEKEN BOL TGO CMR THA NIC CHN MAR COL PRYGTM HND SLV PER DOM VEN PHL MEXECU IRN DZA LKA

TUR

MYSTTO ZAF

0

5

MOZ TZA BGD SLEUGA SEN PAK IDN ZMB GHA IND

80 IDN DOM BOL COL ZMB PAK HNDBGD PER PHL JAM GTM IRN THA SYR ECU VEN SLV LKA GRC PAN BRA DZA CHL URY MEX MAREGY KOR CRI ARG TUN PRT

30

4

D. Non-contributor to Pension Scheme 100

CMR

50 40

PRY SLE

Predicted values (Higher, more informality)

C. Self Employment 60

TGO

Actual values (Higher, more informality)

ZAR

IDN

ESP PRT JOR BEL KWT SYR CHL IRN SWE NOR FIN DNK HKG DEU CAN IRL FRA CHN AUS SGP NLD NZL GBR AUT CHE JPN USA

20

B. Heritage Foundation Informal Market index 5

BOL PAN PERTZA

AUT BELFRA ITA ESP FIN SWE DEU DNK GBR USA AUS NLD IRL NOR NZLJPN CHE

0

20

HUN GRC PRT

ARG BRA JAM MYS PAN EGY TUNCRI POL

Predicted values (% of labor force)

40

60

Note: In each graph, a 45-degree line is drawn to show a distance between predicted and actual levels.

80

100

Figure 5.4: Differences in Informality, LAC Countries and Chile A. Schneider Shadow Economy index

B. Heritage Foundation Informal Market index

(% of GDP)

(Higher, 6 more informality)

60

Max

Max 50 GTM

40

COL BOLBRA ARG

DOM ECU

PRY JAM SLV VEN MEXNIC PANPER URY

GUY

CRI

HTI

5

HTI HND

4 3

HND GTM BOL ECU GUY ARG BRACOL DOM CRI

30

CHL

VEN PRY NIC JAMMEX PANPER SLV URY

2

CHL

20 1

Min

Min

10

C. Self Employment

D. Non-contributor to Pension Scheme 120

(% of total employment) 50

40

30

BOL BRACOL ARG

DOM ECU

CRI

GTM HND GUY NIC JAM MEX

80

PRY SLV

BOL DOM ECU BRACOL CRI

VEN

60

PANPER URY

20

HTI

100

HTI GTM HND GUY

Max (% of labor force)

Max

40

NIC JAM MEX

PRY SLV

PANPER

VEN

ARG URY

CHL

CHL 20

10 Min

0

Note: Presented are all predicted levels, which may be above/below the actual max/min values.

Min

Figure 5.5a: Explanation of Differences in Informality, LAC Countries and Chile Schneider Shadow Economy index (% of GDP) ARGENTINA

BOLIVIA

(%)

BRAZIL

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

COLOMBIA

COSTA RICA

(%)

DOMINICAN REPUBLIC

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

ECUADOR

EL SALVADOR

(%)

GUATEMALA

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

GUYANA

HAITI

(%)

HONDURAS

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

JAMAICA

MEXICO

(%)

NICARAGUA

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

PANAMA

PARAGUAY

(%)

PERU

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

URUGUAY

VENEZUELA, RB

(%)

Legend

(%)

80

80

60

60

40

40

Regulatory Freedom

20

20

Education

0

0

-20

-20

Law and Order

Sociodemographic Factors

Figure 5.5b: Explanation of Differences in Informality, LAC Countries and Chile Heritage Foundation Informal Market index (range 1-5: higher, more informality) ARGENTINA

BOLIVIA

(%)

BRAZIL

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

COLOMBIA

COSTA RICA

(%)

DOMINICAN REPUBLIC

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

ECUADOR

EL SALVADOR

(%)

GUATEMALA

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

GUYANA

HAITI

(%)

HONDURAS

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

JAMAICA

MEXICO

(%)

NICARAGUA

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

PANAMA

PARAGUAY

(%)

PERU

(%)

(%)

80

80

80

60

60

60

40

40

40

20

20

20

0

0

0

URUGUAY

VENEZUELA, RB

(%)

Legend

(%)

80

80

60

60

40

40

20

20

Education

0

0

Sociodemographic Factors

Law and Order Regulatory Freedom

Figure 5.5c. Explanation of Differences in Informality, LAC Countries and Chile Self Employment (% of total employment) ARGENTINA

BOLIVIA

BRAZIL

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

COLOMBIA

COSTA RICA

DOMINICAN REPUBLIC

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

ECUADOR

EL SALVADOR

GUATEMALA

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

GUYANA

HAITI

HONDURAS

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

JAMAICA

MEXICO

NICARAGUA

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

PANAMA

PARAGUAY

PERU

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

URUGUAY

VENEZUELA, RB

Legend

80 (%)

80 (%)

60

60

40

40

Regulatory Freedom

20

Education

20 0

Law and Order

0

-20 -20

Sociodemographic Factors

Figure 5.5d: Explanation of Differences in Informality, LAC Countries and Chile Non-contributor to Pension Scheme (% of labor force) ARGENTINA

BOLIVIA

BRAZIL

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

COLOMBIA

COSTA RICA

DOMINICAN REPUBLIC

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

ECUADOR

EL SALVADOR

GUATEMALA

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

GUYANA

HAITI

HONDURAS

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

JAMAICA

MEXICO

NICARAGUA

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

PANAMA

PARAGUAY

PERU

80 (%)

80 (%)

80 (%)

60

60

60

40

40

40

20

20

20

0

0

0

-20

-20

-20

URUGUAY (%) 90

VENEZUELA, RB 60

60 30 0 -25 -50

Legend

80 (%)

Law and Order

40

Regulatory Freedom

20

Education

0 -20

Sociodemographic Factors

Appendix 5.1: Sample of Countries in the Informality Regressions Country Code DZA ARG AUS AUT BGD BEL BOL BWA BRA CMR CAN CHL CHN COL ZAR COG CRI DNK DOM ECU EGY SLV FIN FRA DEU GHA GRC GTM GUY HTI HND HKG HUN ISL IND IDN IRN IRL ITA JAM JPN JOR KEN KOR KWT MWI MYS MLI MEX MAR MOZ NLD NZL NIC NER NOR PAK PAN PNG PRY PER PHL POL PRT SEN SLE SGP ZAF ESP LKA SWE CHE SYR TZA THA TGO TTO TUN TUR UGA ARE GBR USA URY VEN ZMB ZWE

Schneider Shadow Heritage Foundation Non-contributor to Self Employment Economy index Informal Market index Pension Scheme (84 countries) (86 countries) (57 countries) (70 countries) Algeria √ √ √ √ Argentina √ √ √ √ Australia √ √ √ √ Austria √ √ √ √ Bangladesh √ √ √ √ Belgium √ √ √ √ Bolivia √ √ √ √ Botswana √ √ Brazil √ √ √ √ Cameroon √ √ √ √ Canada √ √ √ Chile √ √ √ √ China √ √ √ Colombia √ √ √ √ Congo, Dem. Rep. √ Congo, Rep. √ √ Costa Rica √ √ √ √ Denmark √ √ √ √ Dominican Rep. √ √ √ √ Ecuador √ √ √ √ Egypt, Arab Rep. √ √ √ √ El Salvador √ √ √ √ Finland √ √ √ France √ √ √ √ Germany √ √ √ √ Ghana √ √ √ Greece √ √ √ √ Guatemala √ √ √ √ Guyana √ Haiti √ √ Honduras √ √ √ √ Hong Kong, China √ √ √ Hungary √ √ √ Iceland √ √ India √ √ √ Indonesia √ √ √ √ Iran, Islamic Rep. √ √ √ √ Ireland √ √ √ √ Italy √ √ √ √ Jamaica √ √ √ √ Japan √ √ √ √ Jordan √ √ √ Kenya √ √ √ Korea, Rep. √ √ √ √ Kuwait √ √ Malawi √ √ Malaysia √ √ √ √ Mali √ √ Mexico √ √ √ √ Morocco √ √ √ √ Mozambique √ √ √ Netherlands √ √ √ √ New Zealand √ √ √ √ Nicaragua √ √ √ Niger √ √ Norway √ √ √ √ Pakistan √ √ √ √ Panama √ √ √ √ Papua New Guinea √ √ Paraguay √ √ √ Peru √ √ √ √ Philippines √ √ √ √ Poland √ √ √ Portugal √ √ √ √ Senegal √ √ √ Sierra Leone √ √ √ Singapore √ √ √ √ South Africa √ √ √ Spain √ √ √ √ Sri Lanka √ √ √ √ Sweden √ √ √ Switzerland √ √ √ √ Syrian Arab Rep. √ √ √ Tanzania √ √ √ Thailand √ √ √ √ Togo √ √ √ Trinidad and Tobago √ √ Tunisia √ √ √ √ Turkey √ √ √ Uganda √ √ √ United Arab Emirates √ √ United Kingdom √ √ √ √ United States √ √ √ √ Uruguay √ √ √ √ Venezuela, RB √ √ √ √ Zambia √ √ √ √ Zimbabwe √ √ √ Country

Appendix 5.2: Definitions and Sources of Variables Used in Regression Analysis Variable

Definition and Construction

Source

Schneider Shadow Economy index

Estimated shadow economy as the percentage of official GDP. Average of 2001-2002 by country.

Schneider (2004).

Heritage Foundation Informal Market index

An index ranging 1 to 5 with higher values indicating more informal market activity. The scores and criteria are: (i) Very Low: Country has a free-market economy with informal market in such things as drugs and weapons (score is 1); (ii) Low: Country may have some informal market involvement in labor or pirating of intellectual property (score is 2); (iii) Moderate: Country may have some informal market activities in labor, agriculture, and transportation, and moderate levels of intellectual property piracy (score is 3); (iv) High: Country may have substantial levels of informal market activity in such areas as labor, pirated intellectual property, and smuggled consumer goods, and in such services as transportation, electricity, and telecommunications (score is 4); and (v) Very High: Country’s informal market is larger than its formal economy (score is 5). Average of 2000-2005 by country.

Miles, Feulner, and O’Grady (2005).

Self Employment

Self employed workers as the percentage of total employment. Country averages but periods to compute the averages vary by country. Average of 1999-2006 by country, but countries in Europe and Central Asia (ECA) are excluded (Loayza and Rigolini 2006).

ILO. Data retrieved from laborsta.ilo.org.

Non-contributor to Pension Scheme

Labor force not contributing to a pension scheme as the percentage of total labor force. Average of 1993-2005 by country.

World Development Indicators, various years.

Per Capita GDP Growth

Log difference of real GDP per capita (2000 US$).

World Development Indicators, various years.

Initial GDP per capita

Real GDP per capita (2000 US$) in 1985, in logs.

World Development Indicators, various years.

Initial Government Expenditure

Ratio of general government final consumption expenditure to GDP in 1985.

World Development Indicators, various years.

Poverty Headcount index

The fraction of the population with income below a given poverty line. The poverty line is $1 per person a day, converted into local currency using a PPP-adjusted exchange rate. The latest/final year of each country’s poverty spell is used.

Loayza and Raddatz (2006).

Initial Gini index

A measure of income inequality ranging 0 to 100 with higher values indicating more inequal income distribution. The initial year of each country’s poverty spell is used.

Loayza and Raddatz (2006).

Law and Order

An index ranging 0 to 6 with higher values indicating better governance. Law and Order are assessed separately, with each sub-component comprising 0 to 3 points. Assessment of Law focuses on the legal system, while Order is rated by popular observance of the law. Average of 2000-2005 by country.

ICRG. Data retrieved from www.icrgonline.com.

Business Regulatory Freedom

An index ranging 0 to 10 with higher values indicating less regulated. It is composed of following indicators: (i) Price controls: extent to which businesses are free to set their own prices; (ii) Burden of regulation / Administrative Conditions/Entry of New Business; (iii) Time with government bureaucracy: senior management spends a substantial amount of time dealing with government bureaucracy; (iv) Starting a new business: starting a new business is generally easy; and (v) Irregular payments: irregular, additional payments connected with import and export permits, business licenses, exchange controls, tax assessments, police protection, or loan applications are very rare. Average of 2000-2005 by country.

Gwartney, Lawson, Sobel, and Leeson (2007), The Fraser Institute. Data retrieved from www.freetheworld.com.

Average Years of Secondary Schooling

Average years of secondary schooling in the population aged 15 and over. The most recent score in each country is used, while figures are computed for countries data are not available.

Barro and Lee (1993, 2001); and authors’ calculations.

Sociodemographic Factors

Simple average of following three variables: (i) Youth (aged 10-24) population as the percentage of total population; (ii) Rural population as the percentage of total population; and (iii) Agriculture as the percentage of GDP. All three variables are standardized before the average is taken. Average of 2000-2005 by country.

Authors’ calculations with data from World Development Indicators, ILO and UN.

Appendix 5.3: Descriptive Statistics Data in country averages; periods vary by informality measure (a) Univariate (regression sample) Variable

Obs.

Mean

Std. Dev.

84

32.960

14.735

8.550

68.200

86

3.055

1.251

1.000

5.000

Self Employment (% of total employment)

57

26.204

12.028

7.132

59.335

Non-contributor to Pension Scheme (% of labor force)

70

53.198

33.482

1.450

98.000

Obs.

Mean

Std. Dev.

145

34.838

13.214

8.550

68.200

159

3.409

1.201

1.000

5.000

Self Employment (% of total employment)

86

25.158

12.118

1.119

59.335

Non-contributor to Pension Scheme (% of labor force)

110

55.999

31.905

1.450

98.500

Schneider Shadow Economy index (% of GDP) Heritage Foundation Informal Market index (range 1-5: higher, more informality)

Minimum Maximum

(b) Univariate (full sample) Variable Schneider Shadow Economy index (% of GDP) Heritage Foundation Informal Market index (range 1-5: higher, more informality)

Minimum Maximum

(c) Bivariate Correlations between Informality Measures (upper triangle for regression sample (in italics) and lower triangle for full sample) Variable

Schneider Heritage Fndn. Self Non-contributor Shadow Economy Informal Market Employment to Pension

Schneider Shadow Economy index (% of GDP)

1.00 145 | 84

0.68*** 83

0.71*** 55

0.72*** 70

Heritage Foundation Informal Market index (range 1-5: higher, more informality)

0.65*** 132

1.00 159 | 86

0.88*** 57

0.90*** 70

Self Employment (% of total employment)

0.65*** 69

0.79*** 76

1.00 86 | 57

0.89*** 51

Non-contributor to Pension Scheme (% of labor force)

0.59*** 104

0.77*** 107

0.88*** 57

1.00 110 | 70

Notes: 1. Sample sizes are presented below the corresponding coefficients. 2. *** denotes significance at the 1 percent level.